{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation\n",
    "\n",
    "This notebook follows up `model_options.ipynb`.\n",
    "\n",
    "The key difference is that we filter using the category distance metric (see `bin/wp-get-links` for details), rather than relying solely on the regression to pick relevant articles. Thus, we want to decide what an appropriate category distance threshold is.\n",
    "\n",
    "Our hope is that by adding this filter, we can now finalize the regression algorithm selection and configuration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "(You may want to read this section last, as it refers to the full analysis below.)\n",
    "\n",
    "### Q1. Which algorithm?\n",
    "\n",
    "In `model_options`, we boiled down the selection to three choices:\n",
    "\n",
    "1. Lasso, normalized, positive, auto 𝛼 (LA_NPA)\n",
    "2. Elastic net, normalized, positive, auto 𝛼, auto 𝜌 (EN_NPAA)\n",
    "3. Elastic net, normalized, positive, auto 𝛼, manual 𝜌 = ½ (EN_NPAM)\n",
    "\n",
    "The results below suggest three conclusions:\n",
    "\n",
    "1. LA_NPA vs. EN_NPAA:\n",
    "   * EN_NPAA has (probably insignificantly) better RMSE.\n",
    "   * Forecasts look almost identical.\n",
    "   * EN_NPAA chooses a more reasonable-feeling number of articles.\n",
    "   * EN_NPAA is more principled (lasso vs. elastic net).\n",
    "2. EN_NPAM vs. EN_NPAA:\n",
    "   * EN_NPAA has better RMSE.\n",
    "   * Forecasts look almost identical, except EN_NPAM has some spikes in the 2014–2015 season, which probably accounts for the RMSE difference.\n",
    "   * EN_NPAA chooses fewer articles, though EN_NPAM does not feel excessive.\n",
    "   * EN_NPAA is more principles (manual 𝜌 vs. auto).\n",
    "   \n",
    "On balance, **EN_NPAA seems the best choice**, based on principles and article quantity rather than results, which are nearly the same across the board.\n",
    "\n",
    "### Q2. What distance threshold?\n",
    "\n",
    "Observations for EN_NPAA at distance threshold 1, 2, 3:\n",
    "\n",
    "* d = 2 is where RMSE reaches its minimum, and it stays more or less the same all the way through d = 8.\n",
    "* d = 2 and 3 have nearly identical-looking predictions.\n",
    "* d = 2 and 3 have very similar articles and coefficient ranking. Of the 10 and 13 articles respectively, 9 are shared and in almost the same order.\n",
    "\n",
    "These suggests that the actual models for d = 2..8 are very similar. Further:\n",
    "\n",
    "* d = 2 or 3 does not have the spikes in 3rd season that d = 1 does. This suggests that the larger number of articles gives a more robust model.\n",
    "* d = 2 or 3 matches the overall shape of the outbreak better than d = 1, though the latter gets the peak intensity more correct in the 1st season.\n",
    "\n",
    "Finally, examining the article counts in `COUNTS` and `COUNTS_CUM`, d = 2 would give very small input sets in some of the sparser cases, while d = 3 seems safer (e.g., \"es+Infecciones por clamidias\" and \"he+שעלת\"). On the other hand, Arabic seems to have a shallower category structure, and d = 3 would capture most articles.\n",
    "\n",
    "On balance, **d = 3 seems the better choice**. It performs as well as d = 2, without catching irrelevant articles, and d = 2 seems too few articles in several cases. The Arabic situation is a bit of an unknown, as none of us speak Arabic, but erring on the side of too many articles seems less risky than clearly too few.\n",
    "\n",
    "### Q3. What value of 𝜌?\n",
    "\n",
    "In both this notebook and `model_options`, every auto-selected 𝜌 has been 0.9, i.e., mostly lasso. Thus, we will **fix 𝜌 = 0.9** for performance reasons.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "We select **EN_NPAM with 𝜌 = 0.9**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preamble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import collections\n",
    "import gzip\n",
    "import pickle\n",
    "import os\n",
    "import urllib.parse\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import sklearn.linear_model\n",
    "\n",
    "DATA_PATH = os.environ['WEIRD_AL_YANKOVIC']\n",
    "plt.rcParams['figure.figsize'] = (12, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load, preprocess, and clean data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and preprocess the truth spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start\n",
       "2010-07-04/2010-07-10    0.941927\n",
       "2010-07-11/2010-07-17    0.880415\n",
       "2010-07-18/2010-07-24    0.842615\n",
       "2010-07-25/2010-07-31    0.858382\n",
       "2010-08-01/2010-08-07    0.833830\n",
       "Freq: W-SAT, Name: us+influenza, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth = pd.read_excel(DATA_PATH + '/truth.xlsx', index_col=0)\n",
    "TRUTH_FLU = truth.loc[:,'us+influenza']  # pull Series\n",
    "TRUTH_FLU.index = TRUTH_FLU.index.to_period('W-SAT')\n",
    "TRUTH_FLU.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Wikipedia link data. We convert percent-encoded URLs to Unicode strings for convenience of display."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def unquote(url):\n",
    "   (lang, url) = url.split('+', 1)\n",
    "   url = urllib.parse.unquote(url)\n",
    "   url = url.replace('_', ' ')\n",
    "   return (lang + '+' + url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raw_graph = pickle.load(gzip.open(DATA_PATH + '/articles/wiki-graph.pkl.gz'))\n",
    "GRAPH = dict()\n",
    "for root in raw_graph.keys():\n",
    "   unroot = unquote(root)\n",
    "   GRAPH[unroot] = { unquote(a): d for (a, d) in raw_graph[root].items() }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load all the time series. Most of the 4,299 identified articles were in the data set.\n",
    "\n",
    "Note that in contrast to `model_options`, we do not remove any time series by the fraction that they are zero. The results seem good anyway. This filter also may not apply well to the main experiment, because the training periods are often not long enough to make it meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4179"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TS_ALL = pd.read_csv(DATA_PATH + '/tsv/forecasting_W-SAT.norm.tsv',\n",
    "                     sep='\\t', index_col=0, parse_dates=True)\n",
    "TS_ALL.index = TS_ALL.index.to_period('W-SAT')\n",
    "TS_ALL.rename(columns=lambda x: unquote(x[:-5]), inplace=True)\n",
    "len(TS_ALL.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(TS_ALL, TRUTH_FLU) = TS_ALL.align(TRUTH_FLU, axis=0, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32e9f3f898>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IEHPYYRy8vmIFdyWUaCo74imW2GomaXpJtRUBAE4/HVi2DPjpp6q1PwVV2WLp8OHDK3xu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\niESCElsRERERiQQltiIiIiISCf8PddySvO17vu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3310704668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TRUTH_FLU.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f32e9d0e160>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MhEeZRUSk51LCLCISwxf0hRPmJE+SPvgnItLDKWEWEYkRNcKsD/6JiPR4SphF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\nDzzXYJZgwHTLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f33107044e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TS_ALL.iloc[:,:5].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize distance from root"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of articles by distance from each root."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ar+إنفلونزا</th>\n",
       "      <td>76</td>\n",
       "      <td>144</td>\n",
       "      <td>73</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+حصبة</th>\n",
       "      <td>39</td>\n",
       "      <td>104</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+حمى الضنك</th>\n",
       "      <td>20</td>\n",
       "      <td>101</td>\n",
       "      <td>105</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+داء المتدثرات</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+سعال ديكي</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+ملاريا</th>\n",
       "      <td>24</td>\n",
       "      <td>71</td>\n",
       "      <td>47</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Chlamydiose</th>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Denguefieber</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Influenza</th>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Keuchhusten</th>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Malaria</th>\n",
       "      <td>44</td>\n",
       "      <td>22</td>\n",
       "      <td>107</td>\n",
       "      <td>40</td>\n",
       "      <td>43</td>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Masern</th>\n",
       "      <td>32</td>\n",
       "      <td>48</td>\n",
       "      <td>97</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Chlamydia infection</th>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>59</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Dengue fever</th>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>43</td>\n",
       "      <td>146</td>\n",
       "      <td>93</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Influenza</th>\n",
       "      <td>32</td>\n",
       "      <td>131</td>\n",
       "      <td>223</td>\n",
       "      <td>119</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Malaria</th>\n",
       "      <td>52</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>167</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Measles</th>\n",
       "      <td>77</td>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Pertussis</th>\n",
       "      <td>35</td>\n",
       "      <td>80</td>\n",
       "      <td>69</td>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Dengue</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Gripe</th>\n",
       "      <td>31</td>\n",
       "      <td>74</td>\n",
       "      <td>124</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Infecciones por clamidias</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Malaria</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Sarampión</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Tos ferina</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+חצבת</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+כלמידיה</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+מלריה</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+קדחת דנגי</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+שעלת</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+שפעת</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               1    2    3    4   5   6   7  8\n",
       "ar+إنفلونزا                   76  144   73    9   1   0   0  0\n",
       "ar+حصبة                       39  104   31    6   3   0   0  0\n",
       "ar+حمى الضنك                  20  101  105   53   4   3   0  0\n",
       "ar+داء المتدثرات               4    3    4    6   0   0   0  0\n",
       "ar+سعال ديكي                   5   20   25    6   0   2   0  0\n",
       "ar+ملاريا                     24   71   47    7   2   0   0  0\n",
       "de+Chlamydiose                13   28   54   15  16   1   0  0\n",
       "de+Denguefieber               12   10   60   24  19  38   1  2\n",
       "de+Influenza                   9   14   26   51  18  13   2  0\n",
       "de+Keuchhusten                10   21   47   13  10   0   0  0\n",
       "de+Malaria                    44   22  107   40  43  57   7  6\n",
       "de+Masern                     32   48   97   23  37  13   1  0\n",
       "en+Chlamydia infection        31   22   59   24   7   2   0  0\n",
       "en+Dengue fever                7   67   43  146  93  18   4  0\n",
       "en+Influenza                  32  131  223  119  59   9   1  0\n",
       "en+Malaria                    52   80   98  167  35   2   1  0\n",
       "en+Measles                    77   55   62   56   8   2   0  0\n",
       "en+Pertussis                  35   80   69   93   9   3   1  1\n",
       "es+Dengue                     11   22   57   58  29  34  20  5\n",
       "es+Gripe                      31   74  124   74  70  17   2  3\n",
       "es+Infecciones por clamidias   4    8    3   13   2   4   0  0\n",
       "es+Malaria                    11    6   11   50  30  32   3  1\n",
       "es+Sarampión                   5    8   10   47  11  12  18  1\n",
       "es+Tos ferina                  5   13   46   32  11  17   4  3\n",
       "he+חצבת                        5    6    6   13   0   2   0  0\n",
       "he+כלמידיה                     2    0    9   19   0   0   0  0\n",
       "he+מלריה                       8    6    9   44  38  12   0  1\n",
       "he+קדחת דנגי                   5   10    8    4   1   3   0  0\n",
       "he+שעלת                        1    0    6   17   3   2   0  0\n",
       "he+שפעת                        6   13   36   20   9   4   0  1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COUNTS = pd.DataFrame(columns=range(1,9), index=sorted(GRAPH.keys()))\n",
    "COUNTS.fillna(0, inplace=True)\n",
    "for (root, leaves) in GRAPH.items():\n",
    "   for (leaf, dist) in leaves.items():\n",
    "      COUNTS[dist][root] += 1\n",
    "COUNTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of articles of at most the given distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ar+إنفلونزا</th>\n",
       "      <td>76</td>\n",
       "      <td>220</td>\n",
       "      <td>293</td>\n",
       "      <td>302</td>\n",
       "      <td>303</td>\n",
       "      <td>303</td>\n",
       "      <td>303</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+حصبة</th>\n",
       "      <td>39</td>\n",
       "      <td>143</td>\n",
       "      <td>174</td>\n",
       "      <td>180</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+حمى الضنك</th>\n",
       "      <td>20</td>\n",
       "      <td>121</td>\n",
       "      <td>226</td>\n",
       "      <td>279</td>\n",
       "      <td>283</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+داء المتدثرات</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+سعال ديكي</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ar+ملاريا</th>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>142</td>\n",
       "      <td>149</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Chlamydiose</th>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "      <td>95</td>\n",
       "      <td>110</td>\n",
       "      <td>126</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Denguefieber</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>82</td>\n",
       "      <td>106</td>\n",
       "      <td>125</td>\n",
       "      <td>163</td>\n",
       "      <td>164</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Influenza</th>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "      <td>100</td>\n",
       "      <td>118</td>\n",
       "      <td>131</td>\n",
       "      <td>133</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Keuchhusten</th>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>78</td>\n",
       "      <td>91</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Malaria</th>\n",
       "      <td>44</td>\n",
       "      <td>66</td>\n",
       "      <td>173</td>\n",
       "      <td>213</td>\n",
       "      <td>256</td>\n",
       "      <td>313</td>\n",
       "      <td>320</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de+Masern</th>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "      <td>177</td>\n",
       "      <td>200</td>\n",
       "      <td>237</td>\n",
       "      <td>250</td>\n",
       "      <td>251</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Chlamydia infection</th>\n",
       "      <td>31</td>\n",
       "      <td>53</td>\n",
       "      <td>112</td>\n",
       "      <td>136</td>\n",
       "      <td>143</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Dengue fever</th>\n",
       "      <td>7</td>\n",
       "      <td>74</td>\n",
       "      <td>117</td>\n",
       "      <td>263</td>\n",
       "      <td>356</td>\n",
       "      <td>374</td>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Influenza</th>\n",
       "      <td>32</td>\n",
       "      <td>163</td>\n",
       "      <td>386</td>\n",
       "      <td>505</td>\n",
       "      <td>564</td>\n",
       "      <td>573</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Malaria</th>\n",
       "      <td>52</td>\n",
       "      <td>132</td>\n",
       "      <td>230</td>\n",
       "      <td>397</td>\n",
       "      <td>432</td>\n",
       "      <td>434</td>\n",
       "      <td>435</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Measles</th>\n",
       "      <td>77</td>\n",
       "      <td>132</td>\n",
       "      <td>194</td>\n",
       "      <td>250</td>\n",
       "      <td>258</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en+Pertussis</th>\n",
       "      <td>35</td>\n",
       "      <td>115</td>\n",
       "      <td>184</td>\n",
       "      <td>277</td>\n",
       "      <td>286</td>\n",
       "      <td>289</td>\n",
       "      <td>290</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Dengue</th>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>90</td>\n",
       "      <td>148</td>\n",
       "      <td>177</td>\n",
       "      <td>211</td>\n",
       "      <td>231</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Gripe</th>\n",
       "      <td>31</td>\n",
       "      <td>105</td>\n",
       "      <td>229</td>\n",
       "      <td>303</td>\n",
       "      <td>373</td>\n",
       "      <td>390</td>\n",
       "      <td>392</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Infecciones por clamidias</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Malaria</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>78</td>\n",
       "      <td>108</td>\n",
       "      <td>140</td>\n",
       "      <td>143</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Sarampión</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "      <td>93</td>\n",
       "      <td>111</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>es+Tos ferina</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>64</td>\n",
       "      <td>96</td>\n",
       "      <td>107</td>\n",
       "      <td>124</td>\n",
       "      <td>128</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+חצבת</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+כלמידיה</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+מלריה</th>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>67</td>\n",
       "      <td>105</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+קדחת דנגי</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+שעלת</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>he+שפעת</th>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               1    2    3    4    5    6    7    8\n",
       "ar+إنفلونزا                   76  220  293  302  303  303  303  303\n",
       "ar+حصبة                       39  143  174  180  183  183  183  183\n",
       "ar+حمى الضنك                  20  121  226  279  283  286  286  286\n",
       "ar+داء المتدثرات               4    7   11   17   17   17   17   17\n",
       "ar+سعال ديكي                   5   25   50   56   56   58   58   58\n",
       "ar+ملاريا                     24   95  142  149  151  151  151  151\n",
       "de+Chlamydiose                13   41   95  110  126  127  127  127\n",
       "de+Denguefieber               12   22   82  106  125  163  164  166\n",
       "de+Influenza                   9   23   49  100  118  131  133  133\n",
       "de+Keuchhusten                10   31   78   91  101  101  101  101\n",
       "de+Malaria                    44   66  173  213  256  313  320  326\n",
       "de+Masern                     32   80  177  200  237  250  251  251\n",
       "en+Chlamydia infection        31   53  112  136  143  145  145  145\n",
       "en+Dengue fever                7   74  117  263  356  374  378  378\n",
       "en+Influenza                  32  163  386  505  564  573  574  574\n",
       "en+Malaria                    52  132  230  397  432  434  435  435\n",
       "en+Measles                    77  132  194  250  258  260  260  260\n",
       "en+Pertussis                  35  115  184  277  286  289  290  291\n",
       "es+Dengue                     11   33   90  148  177  211  231  236\n",
       "es+Gripe                      31  105  229  303  373  390  392  395\n",
       "es+Infecciones por clamidias   4   12   15   28   30   34   34   34\n",
       "es+Malaria                    11   17   28   78  108  140  143  144\n",
       "es+Sarampión                   5   13   23   70   81   93  111  112\n",
       "es+Tos ferina                  5   18   64   96  107  124  128  131\n",
       "he+חצבת                        5   11   17   30   30   32   32   32\n",
       "he+כלמידיה                     2    2   11   30   30   30   30   30\n",
       "he+מלריה                       8   14   23   67  105  117  117  118\n",
       "he+קדחת דנגי                   5   15   23   27   28   31   31   31\n",
       "he+שעלת                        1    1    7   24   27   29   29   29\n",
       "he+שפעת                        6   19   55   75   84   88   88   89"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "COUNTS_CUM = COUNTS.cumsum(axis=1)\n",
    "COUNTS_CUM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter sweep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the set of articles with maximum category distance from a given root."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def articles_dist(root, dist):\n",
    "   return { a for (a, d) in GRAPH[root].items() if d <= dist }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return time series for articles with a maximum category distance from the given root."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def select_by_distance(root, d):\n",
    "   keep_cols = articles_dist(root, d)\n",
    "   return TS_ALL.filter(items=keep_cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>en+Canine influenza</th>\n",
       "      <th>en+Influenza</th>\n",
       "      <th>en+Influenza treatment</th>\n",
       "      <th>en+Reassortment</th>\n",
       "      <th>en+Swine influenza</th>\n",
       "      <th>en+Antigenic shift</th>\n",
       "      <th>en+Influenzavirus C</th>\n",
       "      <th>en+Influenza research</th>\n",
       "      <th>en+Bronchiolitis</th>\n",
       "      <th>en+Adult T-cell leukemia/lymphoma</th>\n",
       "      <th>...</th>\n",
       "      <th>en+Norovirus</th>\n",
       "      <th>en+Influenza prevention</th>\n",
       "      <th>en+Hepatitis C</th>\n",
       "      <th>en+Cat flu</th>\n",
       "      <th>en+Equine influenza</th>\n",
       "      <th>en+Pandemrix</th>\n",
       "      <th>en+Hepatitis D</th>\n",
       "      <th>en+2007 Australian equine influenza outbreak</th>\n",
       "      <th>en+Common cold</th>\n",
       "      <th>en+Influenza-like illness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-04/2010-07-10</th>\n",
       "      <td>3.677000e-07</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>3.396000e-07</td>\n",
       "      <td>2.251000e-07</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>4.990000e-07</td>\n",
       "      <td>2.345000e-07</td>\n",
       "      <td>1.388000e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3.208000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>7.692000e-08</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>2.214000e-07</td>\n",
       "      <td>2.308000e-07</td>\n",
       "      <td>2.214000e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>8.817000e-08</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>4.071000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-11/2010-07-17</th>\n",
       "      <td>4.334000e-07</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>4.164000e-07</td>\n",
       "      <td>2.550000e-07</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>4.603000e-07</td>\n",
       "      <td>2.062000e-07</td>\n",
       "      <td>1.574000e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>3.975000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>6.476000e-08</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>2.072000e-07</td>\n",
       "      <td>2.989000e-07</td>\n",
       "      <td>2.212000e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>9.863000e-08</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>5.081000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-18/2010-07-24</th>\n",
       "      <td>4.267000e-07</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>4.423000e-07</td>\n",
       "      <td>2.449000e-07</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>6.076000e-07</td>\n",
       "      <td>2.293000e-07</td>\n",
       "      <td>1.541000e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4.552000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>8.308000e-08</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>2.155000e-07</td>\n",
       "      <td>2.709000e-07</td>\n",
       "      <td>1.991000e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.021000e-07</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>5.184000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-25/2010-07-31</th>\n",
       "      <td>4.056000e-07</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>5.073000e-07</td>\n",
       "      <td>2.502000e-07</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>5.498000e-07</td>\n",
       "      <td>2.376000e-07</td>\n",
       "      <td>1.679000e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4.237000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>7.595000e-08</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>2.300000e-07</td>\n",
       "      <td>2.829000e-07</td>\n",
       "      <td>1.965000e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.366000e-07</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>5.498000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01/2010-08-07</th>\n",
       "      <td>4.142000e-07</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>4.966000e-07</td>\n",
       "      <td>3.056000e-07</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>5.495000e-07</td>\n",
       "      <td>2.596000e-07</td>\n",
       "      <td>1.607000e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>4.609000e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1.174000e-07</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>2.177000e-07</td>\n",
       "      <td>3.091000e-07</td>\n",
       "      <td>1.916000e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.106000e-07</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>6.181000e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       en+Canine influenza  en+Influenza  \\\n",
       "2010-07-04/2010-07-10         3.677000e-07      0.000010   \n",
       "2010-07-11/2010-07-17         4.334000e-07      0.000012   \n",
       "2010-07-18/2010-07-24         4.267000e-07      0.000012   \n",
       "2010-07-25/2010-07-31         4.056000e-07      0.000012   \n",
       "2010-08-01/2010-08-07         4.142000e-07      0.000013   \n",
       "\n",
       "                       en+Influenza treatment  en+Reassortment  \\\n",
       "2010-07-04/2010-07-10            3.396000e-07     2.251000e-07   \n",
       "2010-07-11/2010-07-17            4.164000e-07     2.550000e-07   \n",
       "2010-07-18/2010-07-24            4.423000e-07     2.449000e-07   \n",
       "2010-07-25/2010-07-31            5.073000e-07     2.502000e-07   \n",
       "2010-08-01/2010-08-07            4.966000e-07     3.056000e-07   \n",
       "\n",
       "                       en+Swine influenza  en+Antigenic shift  \\\n",
       "2010-07-04/2010-07-10            0.000008        4.990000e-07   \n",
       "2010-07-11/2010-07-17            0.000009        4.603000e-07   \n",
       "2010-07-18/2010-07-24            0.000018        6.076000e-07   \n",
       "2010-07-25/2010-07-31            0.000009        5.498000e-07   \n",
       "2010-08-01/2010-08-07            0.000012        5.495000e-07   \n",
       "\n",
       "                       en+Influenzavirus C  en+Influenza research  \\\n",
       "2010-07-04/2010-07-10         2.345000e-07           1.388000e-07   \n",
       "2010-07-11/2010-07-17         2.062000e-07           1.574000e-07   \n",
       "2010-07-18/2010-07-24         2.293000e-07           1.541000e-07   \n",
       "2010-07-25/2010-07-31         2.376000e-07           1.679000e-07   \n",
       "2010-08-01/2010-08-07         2.596000e-07           1.607000e-07   \n",
       "\n",
       "                       en+Bronchiolitis  en+Adult T-cell leukemia/lymphoma  \\\n",
       "2010-07-04/2010-07-10          0.000001                       3.208000e-07   \n",
       "2010-07-11/2010-07-17          0.000001                       3.975000e-07   \n",
       "2010-07-18/2010-07-24          0.000001                       4.552000e-07   \n",
       "2010-07-25/2010-07-31          0.000001                       4.237000e-07   \n",
       "2010-08-01/2010-08-07          0.000001                       4.609000e-07   \n",
       "\n",
       "                                 ...              en+Norovirus  \\\n",
       "2010-07-04/2010-07-10            ...                  0.000007   \n",
       "2010-07-11/2010-07-17            ...                  0.000005   \n",
       "2010-07-18/2010-07-24            ...                  0.000007   \n",
       "2010-07-25/2010-07-31            ...                  0.000005   \n",
       "2010-08-01/2010-08-07            ...                  0.000004   \n",
       "\n",
       "                       en+Influenza prevention  en+Hepatitis C    en+Cat flu  \\\n",
       "2010-07-04/2010-07-10             7.692000e-08        0.000021  2.214000e-07   \n",
       "2010-07-11/2010-07-17             6.476000e-08        0.000026  2.072000e-07   \n",
       "2010-07-18/2010-07-24             8.308000e-08        0.000028  2.155000e-07   \n",
       "2010-07-25/2010-07-31             7.595000e-08        0.000027  2.300000e-07   \n",
       "2010-08-01/2010-08-07             1.174000e-07        0.000026  2.177000e-07   \n",
       "\n",
       "                       en+Equine influenza  en+Pandemrix  en+Hepatitis D  \\\n",
       "2010-07-04/2010-07-10         2.308000e-07  2.214000e-07        0.000002   \n",
       "2010-07-11/2010-07-17         2.989000e-07  2.212000e-07        0.000002   \n",
       "2010-07-18/2010-07-24         2.709000e-07  1.991000e-07        0.000002   \n",
       "2010-07-25/2010-07-31         2.829000e-07  1.965000e-07        0.000002   \n",
       "2010-08-01/2010-08-07         3.091000e-07  1.916000e-07        0.000002   \n",
       "\n",
       "                       en+2007 Australian equine influenza outbreak  \\\n",
       "2010-07-04/2010-07-10                                  8.817000e-08   \n",
       "2010-07-11/2010-07-17                                  9.863000e-08   \n",
       "2010-07-18/2010-07-24                                  1.021000e-07   \n",
       "2010-07-25/2010-07-31                                  1.366000e-07   \n",
       "2010-08-01/2010-08-07                                  1.106000e-07   \n",
       "\n",
       "                       en+Common cold  en+Influenza-like illness  \n",
       "2010-07-04/2010-07-10        0.000009               4.071000e-07  \n",
       "2010-07-11/2010-07-17        0.000009               5.081000e-07  \n",
       "2010-07-18/2010-07-24        0.000010               5.184000e-07  \n",
       "2010-07-25/2010-07-31        0.000010               5.498000e-07  \n",
       "2010-08-01/2010-08-07        0.000010               6.181000e-07  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_by_distance('en+Influenza', 1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit function. The core is the same as the `model_options` one, with non-constant training series set and a richer summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def fit(root, train_week_ct, d, alg, plot=True):\n",
    "   ts_all = select_by_distance(root, d)\n",
    "   ts_train = ts_all.iloc[:train_week_ct,:]\n",
    "   truth_train = TRUTH_FLU.iloc[:train_week_ct]\n",
    "   m = alg.fit(ts_train, truth_train)\n",
    "   m.input_ct = len(ts_all.columns)\n",
    "   pred = m.predict(ts_all)\n",
    "   pred_s = pd.Series(pred, index=TRUTH_FLU.index)\n",
    "   m.r = TRUTH_FLU.corr(pred_s)\n",
    "   m.rmse = ((TRUTH_FLU - pred_s)**2).mean()\n",
    "   m.nonzero = np.count_nonzero(m.coef_)\n",
    "   if (not hasattr(m, 'l1_ratio_')):\n",
    "      m.l1_ratio_ = -1\n",
    "   # this is just a line to show how long the training period is\n",
    "   train_period = TRUTH_FLU.iloc[:train_week_ct].copy(True)\n",
    "   train_period[:] = 0\n",
    "   if (plot):\n",
    "      pd.DataFrame({'truth':       TRUTH_FLU,\n",
    "                    'prediction':  pred,\n",
    "                    'training pd': train_period}).plot(ylim=(-1,9))\n",
    "   sumry = pd.DataFrame({'coefs': m.coef_,\n",
    "                         'coefs_abs': np.abs(m.coef_)},\n",
    "                        index=ts_all.columns)\n",
    "   sumry.sort_values(by='coefs_abs', ascending=False, inplace=True)\n",
    "   sumry = sumry.loc[:, 'coefs']\n",
    "   for a in ('intercept_', 'alpha_', 'l1_ratio_', 'nonzero', 'rmse', 'r', 'input_ct'):\n",
    "      try:\n",
    "         sumry = pd.Series([getattr(m, a)], index=[a]).append(sumry)\n",
    "      except AttributeError:\n",
    "         pass\n",
    "   return (m, pred, sumry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which 𝛼 and 𝜌 to explore? Same as `model_options`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ALPHAS = np.logspace(-15, 2, 25)\n",
    "RHOS = np.linspace(0.1, 0.9, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try all distance filters and summarize the result in a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def fit_summary(root, label, train_week_ct, alg, **kwargs):\n",
    "   result = pd.DataFrame(columns=[[label] * 4,\n",
    "                                  ['input_ct', 'rmse', 'rho', 'nonzero']],\n",
    "                         index=range(1, 9))\n",
    "   preds = dict()\n",
    "   for d in range(1, 9):\n",
    "      (m, preds[d], sumry) = fit(root, train_week_ct, d, alg(**kwargs), plot=False)\n",
    "      result.loc[d,:] = (m.input_ct, m.rmse, m.l1_ratio_, m.nonzero)\n",
    "   return (result, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso, normalized, positive, auto 𝛼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">la_npa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>input_ct</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rho</th>\n",
       "      <th>nonzero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>0.85181</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>0.423396</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>385</td>\n",
       "      <td>0.421616</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504</td>\n",
       "      <td>0.421472</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>562</td>\n",
       "      <td>0.421593</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570</td>\n",
       "      <td>0.422126</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>571</td>\n",
       "      <td>0.422021</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>571</td>\n",
       "      <td>0.421677</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    la_npa                      \n",
       "  input_ct      rmse rho nonzero\n",
       "1       32   0.85181  -1       5\n",
       "2      162  0.423396  -1       6\n",
       "3      385  0.421616  -1       7\n",
       "4      504  0.421472  -1       7\n",
       "5      562  0.421593  -1       7\n",
       "6      570  0.422126  -1       7\n",
       "7      571  0.422021  -1       8\n",
       "8      571  0.421677  -1       7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la_npa = fit_summary('en+Influenza', 'la_npa', 104, sk.linear_model.LassoCV,\n",
    "                      normalize=True, positive=True, alphas=ALPHAS,\n",
    "                      max_iter=1e5, selection='random', n_jobs=-1)\n",
    "la_npa[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                                            32.000000\n",
       "r                                                                    0.591671\n",
       "rmse                                                                 0.851699\n",
       "nonzero                                                              5.000000\n",
       "l1_ratio_                                                           -1.000000\n",
       "alpha_                                                               0.005623\n",
       "intercept_                                                          -0.025942\n",
       "en+Influenzavirus C                                             665758.298050\n",
       "en+Influenzavirus B                                             564721.829660\n",
       "en+Bronchiolitis                                                299406.908302\n",
       "en+Influenza treatment                                           12353.960484\n",
       "en+Influenza                                                      1939.560993\n",
       "en+Canine influenza                                                  0.000000\n",
       "en+Hepatitis C                                                       0.000000\n",
       "en+Influenza virus nucleoprotein                                     0.000000\n",
       "en+Norovirus                                                         0.000000\n",
       "en+Influenza prevention                                              0.000000\n",
       "en+Equine influenza                                                  0.000000\n",
       "en+Cat flu                                                           0.000000\n",
       "en+Rapid influenza diagnostic test                                   0.000000\n",
       "en+Pandemrix                                                         0.000000\n",
       "en+Hepatitis D                                                       0.000000\n",
       "en+2007 Australian equine influenza outbreak                         0.000000\n",
       "en+Common cold                                                       0.000000\n",
       "en+Flu season                                                        0.000000\n",
       "en+Influenza vaccine                                                 0.000000\n",
       "en+Historical annual reformulations of the influenza vaccine         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z961btw6rVq1CTEwM0tLSMHfuXOv/QEIIIYQQKsnb7qv0Th5TFMt8FRER5RiKglmzZqFc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\ngcQWAAAAmfBfRwB61+H04fgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9bd5160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 1,\n",
    "                sk.linear_model.LassoCV(normalize=True, positive=True, alphas=ALPHAS,\n",
    "                                        max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                   162.000000\n",
       "r                                            0.808813\n",
       "rmse                                         0.423246\n",
       "nonzero                                      6.000000\n",
       "l1_ratio_                                   -1.000000\n",
       "alpha_                                       0.005623\n",
       "intercept_                                   0.040238\n",
       "en+Influenzavirus B                     308839.224752\n",
       "en+Oseltamivir                          242121.103904\n",
       "en+Astrovirus                           206288.629263\n",
       "en+Human respiratory syncytial virus     95860.294588\n",
       "en+Bronchiolitis                         71425.420044\n",
       "en+Influenza                               774.419155\n",
       "en+Interferon                                0.000000\n",
       "en+Infectious mononucleosis                  0.000000\n",
       "en+Viral disease                             0.000000\n",
       "en+Sore throat                               0.000000\n",
       "en+Richard Shope                             0.000000\n",
       "en+Cholera                                   0.000000\n",
       "en+Baritosis                                 0.000000\n",
       "en+Fluzone                                   0.000000\n",
       "en+Laryngeal cyst                            0.000000\n",
       "en+M2 proton channel                         0.000000\n",
       "en+Retropharyngeal abscess                   0.000000\n",
       "en+Poliovirus                                0.000000\n",
       "en+Coronavirus                               0.000000\n",
       "en+Canine influenza                          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ll7F3714Auper7dWrF2bNmoXhw4cjLi4OAQEB2du2bt2KjRs3IjIyEikpKfjss8+M/wWp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\n/h+i7ZM6tnmI2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9a8e518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 2,\n",
    "                sk.linear_model.LassoCV(normalize=True, positive=True, alphas=ALPHAS,\n",
    "                                        max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                    385.000000\n",
       "r                                             0.810147\n",
       "rmse                                          0.422226\n",
       "nonzero                                       8.000000\n",
       "l1_ratio_                                    -1.000000\n",
       "alpha_                                        0.005623\n",
       "intercept_                                    0.023569\n",
       "en+Influenzavirus B                      307278.945067\n",
       "en+Oseltamivir                           248650.955817\n",
       "en+Astrovirus                            192382.030474\n",
       "en+Human respiratory syncytial virus      96685.599159\n",
       "en+Bronchiolitis                          61550.704919\n",
       "en+Croup                                   4297.096784\n",
       "en+Laryngitis                              1257.290744\n",
       "en+Influenzavirus C                         849.631059\n",
       "en+Epizootic                                  0.000000\n",
       "en+Transmission and infection of H5N1         0.000000\n",
       "en+Feline viral rhinotracheitis               0.000000\n",
       "en+Nasal septal hematoma                      0.000000\n",
       "en+M2 proton channel                          0.000000\n",
       "en+Weight loss                                0.000000\n",
       "en+Acute bronchitis                           0.000000\n",
       "en+Infectious mononucleosis                   0.000000\n",
       "en+Laryngeal cyst                             0.000000\n",
       "en+Post-polio syndrome                        0.000000\n",
       "en+Influenza A virus subtype H5N3             0.000000\n",
       "en+Richard Shope                              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LREeIaLCxgqnQrl1DesNmuf+1swOsi+hwfqKvG39QRUcbPzYhhNFkXA/EPfsG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\nAACAgkBiCwAAgIJAYgsAAICC8H+uA1qb528sIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9bc2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 3,\n",
    "                sk.linear_model.LassoCV(normalize=True, positive=True, alphas=ALPHAS,\n",
    "                                        max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic net, normalized, positive, auto 𝛼, auto 𝜌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">en_npaa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>input_ct</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rho</th>\n",
       "      <th>nonzero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>0.710605</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>0.389561</td>\n",
       "      <td>0.9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>385</td>\n",
       "      <td>0.390526</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504</td>\n",
       "      <td>0.390381</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>562</td>\n",
       "      <td>0.390718</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570</td>\n",
       "      <td>0.390618</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>571</td>\n",
       "      <td>0.390572</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>571</td>\n",
       "      <td>0.390522</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   en_npaa                       \n",
       "  input_ct      rmse  rho nonzero\n",
       "1       32  0.710605  0.9       5\n",
       "2      162  0.389561  0.9      10\n",
       "3      385  0.390526  0.9      13\n",
       "4      504  0.390381  0.9      13\n",
       "5      562  0.390718  0.9      13\n",
       "6      570  0.390618  0.9      13\n",
       "7      571  0.390572  0.9      13\n",
       "8      571  0.390522  0.9      13"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_npaa = fit_summary('en+Influenza', 'en_npaa', 104, sk.linear_model.ElasticNetCV,\n",
    "                      normalize=True, positive=True, alphas=ALPHAS, l1_ratio=RHOS,\n",
    "                      max_iter=1e5, selection='random', n_jobs=-1)\n",
    "en_npaa[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                                             32.000000\n",
       "r                                                                     0.653963\n",
       "rmse                                                                  0.710464\n",
       "nonzero                                                               5.000000\n",
       "l1_ratio_                                                             0.900000\n",
       "alpha_                                                                0.005623\n",
       "intercept_                                                           -0.201769\n",
       "en+Influenzavirus C                                             1435126.718303\n",
       "en+Influenzavirus B                                              481107.828793\n",
       "en+Bronchiolitis                                                 237769.587471\n",
       "en+Influenza treatment                                           113447.354086\n",
       "en+Influenza                                                       8040.737162\n",
       "en+Canine influenza                                                   0.000000\n",
       "en+Hepatitis C                                                        0.000000\n",
       "en+Influenza virus nucleoprotein                                      0.000000\n",
       "en+Norovirus                                                          0.000000\n",
       "en+Influenza prevention                                               0.000000\n",
       "en+Equine influenza                                                   0.000000\n",
       "en+Cat flu                                                            0.000000\n",
       "en+Rapid influenza diagnostic test                                    0.000000\n",
       "en+Pandemrix                                                          0.000000\n",
       "en+Hepatitis D                                                        0.000000\n",
       "en+2007 Australian equine influenza outbreak                          0.000000\n",
       "en+Common cold                                                        0.000000\n",
       "en+Flu season                                                         0.000000\n",
       "en+Influenza vaccine                                                  0.000000\n",
       "en+Historical annual reformulations of the influenza vaccine          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NMrZC2LS7dwE/P8DRMcvDSUnA/ftArVqG7cYpoB0CnA7j5s2Mx8x0vhKmpq8U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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9af8128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 1,\n",
    "                sk.linear_model.ElasticNetCV(normalize=True, positive=True,\n",
    "                                             alphas=ALPHAS, l1_ratio=RHOS,\n",
    "                                             max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                   162.000000\n",
       "r                                            0.822938\n",
       "rmse                                         0.389294\n",
       "nonzero                                     10.000000\n",
       "l1_ratio_                                    0.900000\n",
       "alpha_                                       0.005623\n",
       "intercept_                                  -0.044469\n",
       "en+Influenzavirus C                     504409.350529\n",
       "en+Influenzavirus B                     259325.879589\n",
       "en+Oseltamivir                          220662.878206\n",
       "en+Astrovirus                            97964.349420\n",
       "en+Human respiratory syncytial virus     93739.267837\n",
       "en+Bronchiolitis                         69069.768615\n",
       "en+Influenza A virus                     18147.456379\n",
       "en+Influenza treatment                    7759.064607\n",
       "en+Influenza                              3736.502697\n",
       "en+Sore throat                            2423.281224\n",
       "en+Richard Shope                             0.000000\n",
       "en+Cholera                                   0.000000\n",
       "en+Baritosis                                 0.000000\n",
       "en+Infectious mononucleosis                  0.000000\n",
       "en+Laryngeal cyst                            0.000000\n",
       "en+Fluzone                                   0.000000\n",
       "en+Pleurisy                                  0.000000\n",
       "en+Kaposi's sarcoma                          0.000000\n",
       "en+Coronavirus                               0.000000\n",
       "en+Canine influenza                          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t0QIIDuYPvVxef50HnJ49W6IQhZpyz4iQSRJb61PSwWOZ6tfnqQKPHCn5scyQ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\nxo0m0YaQW716wPXrPIbw+S5O8B3dBfs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FTZu4H+nKFbp+nefhHTqUb46NNWIcliYjgwdS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\nCSS2AAAAyIT/Bc+yyxbw+a1MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9b715f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 2,\n",
    "                sk.linear_model.ElasticNetCV(normalize=True, positive=True,\n",
    "                                             alphas=ALPHAS, l1_ratio=RHOS,\n",
    "                                             max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                            385.000000\n",
       "r                                                     0.822332\n",
       "rmse                                                  0.390587\n",
       "nonzero                                              13.000000\n",
       "l1_ratio_                                             0.900000\n",
       "alpha_                                                0.005623\n",
       "intercept_                                           -0.067339\n",
       "en+Influenzavirus C                              511059.357752\n",
       "en+Influenzavirus B                              258411.593300\n",
       "en+Oseltamivir                                   220624.342137\n",
       "en+Human respiratory syncytial virus              94172.731116\n",
       "en+Astrovirus                                     74096.910623\n",
       "en+Bronchiolitis                                  68759.890364\n",
       "en+Viral encephalitis                             22021.338431\n",
       "en+Influenza A virus                              19527.220552\n",
       "en+Influenza treatment                             7943.827610\n",
       "en+Laryngitis                                      4096.698231\n",
       "en+Influenza                                       2919.011054\n",
       "en+Croup                                            506.354120\n",
       "en+Bronchitis                                       204.174909\n",
       "en+Gastroenteritis                                    0.000000\n",
       "en+Poliomyelitis                                      0.000000\n",
       "en+Centers for Disease Control and Prevention         0.000000\n",
       "en+Post-polio syndrome                                0.000000\n",
       "en+Fibrothorax                                        0.000000\n",
       "en+Epizootic                                          0.000000\n",
       "en+Transmission and infection of H5N1                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5OwMNu3shIxN8BieEsFp0g3vY1qpVwgO0aweEhfGHXj6vvQbs3g3cvFm6MQoV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\nkNgCAAAgEUhsAQAAkAj/A1PBgLclF9y8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e99c4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 3,\n",
    "                sk.linear_model.ElasticNetCV(normalize=True, positive=True,\n",
    "                                             alphas=ALPHAS, l1_ratio=RHOS,\n",
    "                                             max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic net, normalized, positive, auto 𝛼, manual 𝜌 = ½"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">en_npam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>input_ct</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rho</th>\n",
       "      <th>nonzero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>0.738359</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>0.527247</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>385</td>\n",
       "      <td>0.486709</td>\n",
       "      <td>0.5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504</td>\n",
       "      <td>0.474364</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>562</td>\n",
       "      <td>0.617935</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570</td>\n",
       "      <td>0.618032</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>571</td>\n",
       "      <td>0.617998</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>571</td>\n",
       "      <td>0.61801</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   en_npam                       \n",
       "  input_ct      rmse  rho nonzero\n",
       "1       32  0.738359  0.5      11\n",
       "2      162  0.527247  0.5      17\n",
       "3      385  0.486709  0.5      23\n",
       "4      504  0.474364  0.5      26\n",
       "5      562  0.617935  0.5      18\n",
       "6      570  0.618032  0.5      18\n",
       "7      571  0.617998  0.5      18\n",
       "8      571   0.61801  0.5      18"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_npam = fit_summary('en+Influenza', 'en_npam', 104, sk.linear_model.ElasticNetCV,\n",
    "                      normalize=True, positive=True, alphas=ALPHAS, l1_ratio=0.5,\n",
    "                      max_iter=1e5, selection='random', n_jobs=-1)\n",
    "en_npam[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                                             32.000000\n",
       "r                                                                     0.656821\n",
       "rmse                                                                  0.737210\n",
       "nonzero                                                              11.000000\n",
       "l1_ratio_                                                             0.500000\n",
       "alpha_                                                                0.001101\n",
       "intercept_                                                           -0.474304\n",
       "en+Influenzavirus C                                             1515751.443092\n",
       "en+Influenza virus nucleoprotein                                 635922.462467\n",
       "en+Influenzavirus B                                              513402.566231\n",
       "en+Canine influenza                                              279283.462613\n",
       "en+Equine influenza                                              256546.198822\n",
       "en+Bronchiolitis                                                 236891.976108\n",
       "en+Historical annual reformulations of the influenza vaccine     221985.398538\n",
       "en+Influenza treatment                                           153959.458757\n",
       "en+Pandemrix                                                      30987.615157\n",
       "en+Influenza                                                       4046.397886\n",
       "en+Norovirus                                                       2405.500335\n",
       "en+Hepatitis D                                                        0.000000\n",
       "en+2007 Australian equine influenza outbreak                          0.000000\n",
       "en+Rapid influenza diagnostic test                                    0.000000\n",
       "en+Cat flu                                                            0.000000\n",
       "en+Hepatitis C                                                        0.000000\n",
       "en+Influenza prevention                                               0.000000\n",
       "en+Common cold                                                        0.000000\n",
       "en+Flu season                                                         0.000000\n",
       "en+Influenza vaccine                                                  0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jhhETx3TatAFCQvCUT0RmAu/JE2DvXmDpUkAtq+7at5xlCDoS2Dok3eQxXVeE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\noSx3q6CaEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e98f4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 1,\n",
    "                sk.linear_model.ElasticNetCV(normalize=True, positive=True,\n",
    "                                             alphas=ALPHAS, l1_ratio=0.5,\n",
    "                                             max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                    162.000000\n",
       "r                                             0.772064\n",
       "rmse                                          0.527285\n",
       "nonzero                                      17.000000\n",
       "l1_ratio_                                     0.500000\n",
       "alpha_                                        0.005623\n",
       "intercept_                                   -0.545451\n",
       "en+Influenzavirus C                     1036439.486855\n",
       "en+Patrick Laidlaw                       734337.031866\n",
       "en+Influenzavirus B                      181087.336712\n",
       "en+Influenza research                    172722.446854\n",
       "en+Oseltamivir                           165083.182335\n",
       "en+Influenza treatment                    89639.505487\n",
       "en+Astrovirus                             78061.518304\n",
       "en+Human respiratory syncytial virus      71081.636960\n",
       "en+Bronchiolitis                          66351.250421\n",
       "en+Influenza A virus                      62662.014524\n",
       "en+Sore throat                            35723.657394\n",
       "en+Upper respiratory tract infection      35061.664688\n",
       "en+Anal cancer                             3887.662516\n",
       "en+Influenza                               3875.056241\n",
       "en+Infectious mononucleosis                2592.070007\n",
       "en+Pneumonia                                809.210540\n",
       "en+HIV                                      132.551590\n",
       "en+Hepatitis D                                0.000000\n",
       "en+Pandemrix                                  0.000000\n",
       "en+Viral disease                              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i9u67wBtv8EH6a68BGzYAsVUacWArK5AJYdtCQ3EjqzZq1y7m9XXqbN96C9i+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\nMneNRVFiu3cDT6Ye5yaRtsrXl8sRvvq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eOym2wzOkuhvOO6WkcD1g3768okIB7w8ZGUS9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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e920c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 2,\n",
    "                sk.linear_model.ElasticNetCV(normalize=True, positive=True,\n",
    "                                             alphas=ALPHAS, l1_ratio=0.5,\n",
    "                                             max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input_ct                                    385.000000\n",
       "r                                             0.786864\n",
       "rmse                                          0.486693\n",
       "nonzero                                      23.000000\n",
       "l1_ratio_                                     0.500000\n",
       "alpha_                                        0.005623\n",
       "intercept_                                   -0.672222\n",
       "en+Walter Fiers                         1095035.094420\n",
       "en+Influenzavirus C                      917081.994541\n",
       "en+Fujian flu                            254105.752536\n",
       "en+Influenzavirus B                      178153.262873\n",
       "en+Patrick Laidlaw                       162966.052939\n",
       "en+Oseltamivir                           161783.326776\n",
       "en+Influenza A virus subtype H3N8        107999.040892\n",
       "en+Influenza treatment                    96260.627799\n",
       "en+Viral encephalitis                     94245.866855\n",
       "en+Human respiratory syncytial virus      70575.679240\n",
       "en+Influenza A virus                      64060.398284\n",
       "en+Bronchiolitis                          61627.341787\n",
       "en+Sore throat                            37644.619337\n",
       "en+Upper respiratory tract infection      28821.347578\n",
       "en+Astrovirus                             18900.165961\n",
       "en+Streptococcal pharyngitis               8190.375303\n",
       "en+Anal cancer                             5775.416257\n",
       "en+Bronchitis                              3739.749988\n",
       "en+Rotavirus                               3444.054913\n",
       "en+Influenza                               2719.362384\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RmZmJTz75xPj/ISGEVVJPHgOkzlYIYRnUAa20+yoPRTHMVzkRUaGhKJg3bx6qVKkCFxeX\ncj/m+PHj0bBhQ7i4uGDEiBE4depUuR9LCGHb1KUIgAS2QgjLYM0ZWydzDwCFAktLUKdOnQo/Rq1a\ntfK/r1q1KlJSUir8mEII26QuRQAksBVCWAZrDmzNn7E1I21lBoVvc3V1RVpaWv7Pubm5iI2NLfH+\nQghRFlKKIISwNBLYWqlatWohPDwcAJckkEb2uHHjxsjIyMD27duRk5ODhQsXIqtQwYmvry8iIyOL\n3U8IIfQlpQhCCEsj7b6s1LRp07BgwQJ4enpi8+bNxTKw7u7u+PbbbzFhwgTUqVMHbm5uRUoVhg8f\nDiJCzZo10a5dOwCSxRVClI26jy0gga0QwjJYc8ZWMVS2UVEU0vZYiqJIRtPKyO9MCNNp2hT47Teg\nWTPgv/+ACRMAmW8qhDCnRYuAqVOBefOA2bPNPZri8uIUrZlEu87YCiGEucnkMSGEpZF2X0IIIcql\n8OQxHx8gPh7IyTHvmIQQ9i0rC3Bxsc5SBL0CW0VRPBRF2agoykVFUc4rivKIsQcmhBC2TqUC0tKA\natX4ZycnXlo3Ls684xJC2LesLMDV1YYDWwBfANhGRM0APATgovGGJIQQ9iElBahaFXAodCT28QHu\n3TPfmIQQIjubT7htMrBVFMUdQDciWgEARJRDRElGH5kQQtiiyEggMxNA0VZfahLYCiHMzdYztoEA\n4hRFWaEoyklFUX5QFKWKsQcmhBA2aeJEYNs2ABLYCiFKkXcSbGpZWXw1yVYDWycADwP4hogeBpAG\nYJq+TxAQEABFUeTLir4CAgLK+XYSQpTq/n3g7l0ARXvYqklgK4TI16YNcOGCyZ/WmjO2TnrsEwPg\nBhH9m/fzJgBTte04d+7c/O9DQkIQEhKCyMjICg5RCCFsSEICkLc0t66MbV7cK4SwZ7m5wLVrQGgo\n0Ly5SZ86O5sDW0tp97Vv3z7s27dPr31LDWyJ6K6iKDcURWlMRFcA9AKg9fShcGArhBBCCz0C27yV\nvoUQ9uzePe79d/AglzCZkKWVIqiTpWrz5s3Tua8+GVsAmAzgZ0VRnAGEAxhfgfEJIYR9IgISE/MD\nW22lCL6+UooghABw4wb3/zt40ORPnZXFJ90JCSZ/6grTq90XEZ0movZE1IaIhhLRA2MPTAghbE5y\nMl9eLCVjK4GtEAIxMUC3bkBGBhAdbdKnVpciWErGtixk5TEhhDCVxET+t4SMrQS2QggAnLGtWxfo\n2tXkWVtrnjwmga0QQphKQgJQo4Zek8eIzDA+IYTliIkB6tSRwLaMJLAVQghTSUgAgoJ4zVwirYGt\nqyv/m5pq+uEJISxITIzZMrZSiiCEEKJ0CQlArVpA5cpAUpLWUgRFkXIEIQS4FKFOHe5lGxFh0plc\n6oytpbT7KgsJbIUQwlTUpQje3kBsrNaMLSCdEYQQKMjYOjsDHToAR46Y7Kktrd1XWUhgK4QQpqIR\n2CYnaw9sJWMrhJ3LzQVu3wZq1+afTVyOIKUIQgghSpeYWCxjq1mKAEhgK4Tdu3cPqF4dcHHhnzt0\nAP79t+T7GJBMHhNCCFE6PUsRZFldIeycutWXmr8/cOeOyZ5eAlshhBClS0jgLEyhUgTJ2AohilG3\n+lLz9TXp2a4EtkIIIUpXhoytBLZC2DHNjK23N3D/PpCTY5KnlxpbIYQQpSsU2KruxiIzk2cea5LA\nVgg7p5mxdXLiY0dcnEmeXt0VQdp9CSGE0E0d2Hp5IfdOLNzcuG+tJmn3JYSd0wxsAZOWI1hzKYKT\nuQcghBB2Q90VISUFuXdj4eGhfTeZPCaEndMsRQBMFtgSWXcpggS2QghhCkQFGduMDOTcjkX7EO27\nenkB8fHcytLR0aSjFEJYAm0Z21q1TBLY5ubylSQXFw5sibRfWbJUUooghBCmkJ7Onw6VKwPe3nBO\njEXPntp3dXLi5gn375t2iEIIC6BenMHfv+jtJsrYZmUBlSoBDg58Yp2ba/SnNCgJbIUQwhTU2VoA\ncHUFqQg9H0nVubtMIBPCTmkuzqBmosA2O5sDW4BX87W2cgQJbIUQwhQKBbYRkQruK95oUlP3DGcJ\nbIWwUzduFC9DAEyasXV25u8lsBVCCKFdocB2zx4gy8MbSlyszt2lM4IQdiompvjEMYAPCiZYfUxd\nigBwYGttLb8ksBVCCFNQd0QAsHcv4FSbF2nQRTojCGGnYmKK19cCUoqgJwlshRDCFPIytkScsa0e\nVHpgKxlbIezQ3buAn1/x203UFUFKEYQQQpQuIQGoXh1XrvCHRbUArxID2/r1gUOHuNWOEMKO3L3L\nZ7aa1MvqGrlNgWYpggS2QgghisvL2O7ZA/TsCSg+JWdsR40CUlKApUtNOEYhhPndvctlB5qcnQEP\nD6P3ASwc2FaqJIGtEEIIbfIC29BQICQEnH0pIbB1dgZWrQJmzgQiIkw2SiGEud27pz1jC5ikzlZq\nbIUQQpQub/LYuXNAmzYoNbAFgBYtgHffBcaPB1Qq0wxTCGFmujK2gEkCW6mxFUIIUbqEBOR61MD1\n60BQEPQKbAHgrbe4w8/x48YfohDCzIhKz9gaueWXtPsSQghRuoQE3M2sAW9voGpV8AfUzZulzg5z\ndAQ6dAAuXDDNMIUQZpSSwktvV6umfbsJOiNIKYIQQojSJSQgPKE6mjTJ+7lBA16M/fTpUu/arJkE\ntjZvxw4gOdncoxDmdu+e7jIEQEoR9CCBrbls2gSEhha9bcMG4MgR84xHCGFcCQm4fLdGQWDr4AA8\n8wzw88+l3rV5c+DiReMOT5jZlCnAihXmHoUwN12tvtRMFNhKxlaUDREfxIYOBebP57P08eOB558H\nvvzS3KMTQhhDQgLO3ayBxo0L3fbMM8DatQV9KTMyuKg2M7PIXZs3l4ytzYuLA1auNPcohLlZQMa2\ncCmCtPsS+rl4kac4nz3LSxD5+fEH2+HDnMWVjuxC2JbMTCA7G2fDXQsytgBHrL6+BVdvPvgA+Owz\n4Pffi9w9MJDni6Smmm7IwoSIOLC9d0+v0hRhwywkYyulCKJs/voLeOwxoHZt4J9/gIMHuWFlq1Z8\nefLaNXOPUAhhSHmtvi5fUYoGtkBBOcKlS8B33wGffFJsVQYnJ+6kcPmy6YYsTCgxkWcUjhvHnwXC\nfpXU6guQUgQ9SGBrDn/9BQwaxN87OXFTS0Xhr+7di9feCiGsW0ICVNVrIC4OqFtXY9vTTwO//Qa8\n+CIwaxYwaRJn7cLDi+wm5Qg2LC6O27+NHculKdYWSQjDKanVF8DbYmON2tha2n2JsomPB06dAnr0\n0L5dAlshbE9CAtJdqqNRI27fVUTt2sDDDwPp6cCrrwIuLsCzzwLLlxfZTQJbGxYbC3h5cVq+USPu\nkCDsU2kZ20qVAHd3oy6rK+2+RNns2MHraVapon27OrCVOlshbEdcHB44exUvQ1D75htg8+aCqPeF\nF3iGfE5O/i7NmklnBJulztgCnLWVcgT7VVrGFigoR0hJAcLCgP/+438NFIFKja0om61bC8oQtGnc\nmN9VkZEmG5IQwsgiIhDjHKg7sG3SBAgIKPi5RQv+efv2/JskY2vDYmMLAtu+fXkisbBPpWVsAV6k\n4bHHeL+xY7mM6ckngZkzDTIEqbEV+svJ4YztwIG695E6WyFsT3g4ruY0KNrqqzTjxhXpcRsUBERF\nFesEJmyBuhQB4CLspCTgwQPzjkmYR2ntvgDgq6/4Ck9CAnDmDHDyJE9EX7XKIAWxdtPuS1EUB0VR\nTiqK8qcxB2TT/v0XqFOHv0oiga0QtiU8HGEPGujO2GozYACwe3f+JJFKlYD69YGrV40yQmFOhUsR\nHBw4gy91J/YnK4v72teoUfJ+LVoA7doVRJ8An/k2bcqT0w0wDHspRXgdgFwIq4gzZ3iSSGkksBXC\nplB4OA7dLmPGtm5doGZNnmyaR8oRbFThUgRACqrt1b17/D5wKOfF9AkTgB9/rPAw7KIUQVGUOgAG\nAlhm3OHYNtX5i0jwa1b6js2bc1/DO3eMPyghhHERgcIjcLtyIDw9y3jfvn2BXbvyf5TA1kYVLkUA\nJLC1V/pMHCvJU08BR44AN29WaBj20hXhMwBTAMhU/Qq4f+gi3lrWXGf7ucOHgRMnwHW2LVvKJ5gQ\ntuDuXeS4uMK/qVvZ79unjwS29qBwKQIgga2NS0gA5szRskGfiWMlqVoVGDGiwkszW3sfW6fSdlAU\n5TEAd4nolKIoIQAUXfvOnTs3//uQkBCEhIRUfIQ2pNL1i9if2AxHjgBduhTdlpsLjB/PJ2sHDoBr\naM6fB3r2NMtYhRAGEh6ORM8GaNSoHPcNCQFGjwbS0oCqVdG1K/Daazy3yN3d0AMVZiOlCHYlIoLn\nec2bp7GhohlbAHj+eV70ZcYMTpKVg2aNbVJSxYZkCPv27cO+ffv02rfUwBZAFwCPK4oyEEAVAG6K\noqwmojGaOxYObIWGlBRUTo6FS+P6WLeueGC7cSPg6cmLDZ09C7SS1IwQtiE8HHeqNECDBuW4r5sb\nr0x44ADQrx/q1QP69QN++AF45x2Dj1SYi2YpQqNGQEwMkJEBVK5svnEJo0hJ0REsVjRjCwDt23O6\n9ehRoFOncj2EJZYiaCZL5xU7KyhQaikCEc0gonpE1ADAKAB7tAW1ohSXLiHSOQhz5jtiw4Yifdeh\nUgELF/KliRdeAJYsQUHGVghh3cLDEY5yBrZAsXKEKVOAzz+3vsuDQof0dI4c3AqVqjg7Aw0aAFeu\nmG9cwmhSUzmwLbYOkz6tvkqjKMDIkcD69eV+CLuYPCYqLufsRZzOao7Bg4HAQO7io/b777wQWb9+\n3Gd53TogNaA5B7ayApkQ1i08HOfTDRfYBgdzre3atYYZnjAzdX2t5mVjKUewWSkpXH6Ynq6x4e7d\nipciABzYbtwInRN6SlG4FMGm+9gCABGFEtHjxhqMLUs8chG3qzdD1apc/rJuHd+elQUsWADMmsXH\ntTp1uNvXml15Z2337plv0EKIigsPx7/xFQhs27cHoqP5Qy/Pu+8CH39c7s8tYUk0yxDUJLC1Wamp\n/G+xcgRDlCIA/N6pWRM4eLBcd5eMrdBLxqmLyGrErb5GjgT++IOD2+bN+YrT4MEF+06cCHy3RAFJ\nna0QVk917TrOpzdArVrlfAAnJ6Br17xZpaxXLy69/OMPw4xRmJFmRwQ1CWxtls7A1hCTx9RGjSp3\nOYIl1tiWhQS2JuISfhFVgjmw9fMDOnQAPvoI+O47Xhmv8FWo3r35WPfAX+pshbBq6elAXBwqBfqX\nd4Iy69IFOHQo/0dF4ePHO+/w/CJhxTQ7IqhJYGuzUlL4X6NlbAHOoG3aVHRCj540uyJYWz2/BLam\nkJUF94Qo+D0alH/TH3/w8s59+hTf3cEB6NEDuADJ2Aph1SIjkeYdgPoNHSv2OF27Frus2Ls3N0z4\n+OOKPbQwM12lCE2a8PrJubmmH5MwKq0ZW5VKd/a+PBo25NULy7GKqZQiiNJdu4abjvXQPNgl/6bK\nlUteNS8kBNgXKxlbIaxaeDji3CtQX6vWrh1n79SfiHkWLwa++AKIiqrg4wvz0RXMuLpy9i4iwvRj\nEkalNWMbGwtUr14QURrCyJHAhg1lvpuUIohSZZy8gPO5zRAUVPq+at27AxsvtABJxlYI6xUejhvO\nBghsK1cGWrcGjh8vcnNAADB5svS0tWq6ShEALkc4d8604xFGpzVjGxPDs8cNaehQbrtUxqy/ZGxF\nqeIOXsQ9r2Zw0mc5jDwNGwKxDr5QZedKZwQhrFV4OK5kN0BgoAEeS0s5AsBB7fbtQHKyAZ5DmF5c\nnPZSBAAYNgz49FNp+2hjUlN55cAige3Nm4C/v2GfqGFDoFYt4MiRMt3Nrtp9ifLJPHUR2XkdEfSl\nKED3EAWxPi2kzlYIaxUejtPJBsjYAsUmkKlVrQq0bat1k7AGJWVsx4/n69bluJwsLFdKClC7tgky\ntgBnbX/9tUx3kVIEUaoq18+jysPNy3y/kBDgAjWXOlshrBRdv47jcQbK2HbuzJkXLZcVu3cv1xwR\nYQlKCmwdHYEvv+Tl5tLSTDsuYTSpqdwdyegZWwAYMgT47bcyZf2lFEGULDUVNeKvwbd3qzLftXt3\nIDSuBej7KMnXAAAgAElEQVSs1FgJYXUyM4Hr4bjt3gTVqhng8by9+dNQS82lBLZWrKRSBADo1o1P\naqZPB77+Ghg0KG/ddWGtUlJMGNi2asUnSKdO6X0XafclSpR24D+cU1qhfVeX0nfWEBQEHHXqguxd\ne40wMiGEUZ0/jzS/BqjdsIrhHrNLF611tp06AWfOFGuaICxdbi6QmMirRGlx5kzeFItFi4A9e4D/\n/uM+bwsXlqs/qbAMqakmLEVQlIKsrZ4kYytKdHnVUdyq21HXcatEigLU7BWMnLhE4Pp1ww9OCGE8\np07hrl+wYcoQ1HRMIKtalXvaHj5swOcSxhcfD3h4cEZNixkzgG+/BVCvHnD2LLBiBfDGG0D9+sCW\nLSYdqjAck5YiAGWus5UaW1GitL3H4PXYI+W+f/ceDvjXeyCwbZsBRyWEMLqwMFytFmyYiWNq/fsD\nO3cCDx4U2yTlCFaolDKEK1eAXbu0bJg4kZetFFbJpJPHAOCRR/gkSs+V7DRLESSwtWO5ucA333Bp\nHcBzAgLvHUWbVzqW+zG7dwfWPZDAVgirExaGMGpj2MDWzw/o2xdYtarYJglsrVAJE8eysnjhjTNn\ntJzHPPUU10xevWr8MQqDK5axTUriyV3u7sZ5QgcHYMwYYNmyUnclknZfopD33wfefrugWfr2pTFw\nrZQN1xb1y/2YTZoA/yh9oDpwUArohLAWKhVw5gwOJBs4sAWASZP4DFqlKnJz585AWBiQnm7g5xPG\nU0JgGxHBCbzOnYG9mtMsXFyA55+XSWRWSKXiBhe1ahUKbG/e5F+2ohjviV96CVi9GsjIKHG33Fyu\njFFXx0jG1h7FxwO9e+PqG9/gu+/4g2XbNmDjRuDqT0eR/lDHCr1ZFQV4uIcH7tZtp+XoJoSwSNev\nI8fDE0cue6JFCwM/dpcuXFSrcY26WjWgZUvg6FEDP58wnuhonZefL1/mxEafPjrKEV5+mQMVOZOx\nKunpvJBgjRqFAtuYGOPV16o1aAA8/DCwaVOJuxWeOAZIYGt/IiKALl2QnZCMSz/sx/LlvALihg1c\nAuUbWbH6WrWQEGB/tceArVsrPmYhhPGFheGcYxtMmKC7RWm5KQpnbb/+utimPn2An3828PMJ47ly\nBWjcWOemJk248uTvv7XsEBgItG8vizdYmdRUwNVVY+UxY04cK+zll4Hvvy9xl8JlCIC0+7IviYk8\nQ3nSJHzWcjkeqRSGAQN4U9u2wMcfA4/7HoVTl/LX16p17w78eDuvzlaWVhTC4sXtPoWdscGYPt1I\nT/D005ya1eiW8vbbvLzugQNGel5hWFevcl9HLS5f5pi3VSteLjkiQstOEyfmtU0Q1iIlha+uuLhw\nWUJmJow7caywwYP5mFHCok+FOyIAkrG1L2FhnNp/9VVsOtsENTNvFlmsffyz2agXF8Zn1BXUrBlw\nKrMZclQOsgqZEFYg+o8wBA0PhqenkZ6galWeDPLjj0Vurl6dF6p66aWCSazCgpWQsVWXIigKt67V\nWo4wcCBw5w5w8qRxxykMRp2xVRTO2iYnw3QZW2dnrs3+4Qedu0gpgj27cAFo3hzJycCFK05QWrUE\nTp8u2H72LPcaNMAsR0UBuocouNZsMPDnnxV+PCGE8Rw9CtSJDcPAGW2M+0Tjx3ONpcYSu0OHckA0\ndy4HQ+++C0ydCty9W/whVCpg2jTg2DHjDlVokZ7Ok8fq1dO6uXDM27evjsDW0ZEvL0vrL6uhDmyB\nQuUI6sljpjB2LE8C0ph8qqatFCEnx7ouFktgW14XLgAtWuDYMSA4GHB4OLjoWfOBA7wckIGEhABb\nHR8H/vjDYI8phDC8vevuoJpLNioH1TXuE7VsyT2D/vmnyM2Kwk0T1q7l4LZqVZ4I3bw5B7H37/N+\nRMCbbwJffFHqfBJhDNevc52slsUZHjzgS9bqJF6fPsDu3Tqy8BMm8C8wMdG44xUGoS5FAAoFtqaY\nPKYWFAS4ufFVZy00SxEUBXBysq6F7iSwLa+8jO2hQzxJGcHBRd8of/3Fl4kMpHt3YOmV7lyTdfu2\nwR5XCGFYWcfCkNIo2Lite9TGjePVqDT4+3MP1EOHOLj94gtue5qYWJDNnTED2L8fWLMGOHLE+EMV\nGq5c0Vlfq87Wqt9Cfn78EaN1VVRfX164Y/Vq441VGIzZM7YAMGiQzsnomqUIgPWVI0hgW17nzwPN\nm+PgQS2B7YMHfG2vTx+DPV3z5sD9JGekPdpfllIUwoJVuXIaTu0eMs2TPf00sGMHkJBQ6q5163Lb\n0+PHgchIvtvOnXyZ+9Qp65v5bPX0mDhW2IsvAkuX6nisF18EVq406PCEcWhmbFPuZ/Lfr4+P6QYx\naBAn37TQLEUAJLC1D7GxQHY2crz9cOwYN9BGq1Z8NMrM5E+Mbt0K3r0G4OAA9OgBnPB7XOpshbBQ\nKSlAvaSzqN61lWme0NMT6NcPWLdO77s0aMAxUFgYf5a6uQENG3JwK0zo6tVSW30VNmQIr0Km0QiD\nPfooZ/3Cww0/TmFQmhnb7OjbvFqDgwnDsa5d+f2npfBeV8ZW3xNfjZJ/s5DAtjwuXgSaN8fZcwrq\n1AFq1gRQpQp/Opw/zxnVxx83+NP26wesujeArx+mpBj88YUQFXPhAvCw8zk4PGSiwBbgSWQ//lih\n2R2dOkk5gsmVUIqgLWPr4gI895yOVVGdnDjy3bzZ8OMUBqUZ2NINE7X6KszZma8ob9tWbJNmja16\nd30ytnFxPBfS3PW4EtiWh2YZglpwMHDiBDeSHDTI4E/brx/wZ6gHqMMjOjp2CyHM6fypbNTPvsI9\n+kylTx9eo1NjEllZdOoEHD5swDGJ0pWQsVW3+tKkrjjQGmQ89ZTMArQCmqUIDrfL3uorMpL/5CtE\nRzlCRWpsr18Hbt0C/vuvgmOrIAlsyyOvI8KhQ5zRzxcczKsBBQYaZYZjnTp8xSIq+AnpjiCEBbp7\n8CpSq9fhVgSm4ugIzJ7NM8LKmbXt3FkytiaVlMRftWsX26RS6Y55mzXjC4NayyO7d+dShKgow49X\nGEzhjK2bG+B0r2wTx7KyuNJx0KAKBrcDBnCrDY0ag4rU2EZG8r+7d1dgXAYggW15XLgAaqYjY3vu\nnFHKENT69QP+yu4P7NljtOcQQpRP9qlzyGzc0vRPPGIEEB9f0Ow0NRVYvFjvVRoaNeK2qjdvGnGM\nosC1a/yia+mccfMmZ/J0tUCfNAlYtEjLOYyzM/DEE1KOYOFSUgCf7JtA+/YYvyoE7U58V6ZE2Nq1\nXMHi7w88+SS38isXb2+elT5rFk94z1ORUoSoKD4hM3d4IoFteVy4gCjX5sjJ4bPnfG3yGrIbObD9\n5URDfjfHxBjteYQQZecafg5VOpiwvlatcNY2MpLPuD/6iL/0oChAx46StTWZEsoQTpwAHiqhqcbw\n4Zzs3bFDy0YpR7B4qalAUPRuwMsLZ4fOwYaHP+K2fXpQqfhPesYM7vLn6Qn06gV89RW/b8o8cWvN\nGm4f2rAh8P77gEqltRShUiX9M7ZjxnBTqHIH3AYggW1ZxccDaWn47bg/Bg3SOOGuUYOLsUs6KlVQ\nt27A6TMKstt25CWOhBAWIS4OCMo8C/dOZsjYApy1TUjgK0fjxwP//svr6169qtfdZQKZCZUwcUzd\ngk0XR0c+f5k9W0vWtlcvLtC9ccNgQxWGlZoK+EccBAYMQNojPbC3xlDAy0uv+/75J9fn9urF8wV/\n+omXzz57Fhg5Epg/v/THUKkKtcJv0ID7Hx87xrHLpEnIyqRylyJERQGtWwMtWpj3WCKBbVnlLczw\n2+8KhgzRsn3AAKM2Zq9Shet6L3tKYCuEJTl3DmjjdI6X1zYHR0dg1Sr+9Hv9dW5cO2MGMHGiXrW3\nMoHMhHT0sCXiwLZfv5LvPmwY10IWa2nu7MzZvw8+MNhQhWGlpAA+13hlp/wFGvRABHz4Ia8eqA4x\nnJ15hdwffuAM/pIlpVcfrV8PNG3Kc+DzNWzIk95PnkSbVW+gknPR44W+7b6iooCAAKBnT/OWI0hg\nW1YXLiA9sDnOnOGzJnPo1w/Y+aCTBLZ2JjsbeO89PjtXIwIiIvLilvR0rt0TZnHpZBp8s2N0ZuJM\nokMHvqyjNnkyr6G7dq1ed714kdt0CyPTUYpw5Qq3SmrevOS7OzgA8+Zx1lal0tg4cybX2V64YLjx\nCoNxSIxHldho4KGHdAa2Dx5woLpmDZfKT5rEzU8ePOC6Wm0aN+aLxRs3lvz8W7ZwpdKgQRptbN3d\ngZ074X3lMAZd/qTIffTJ2BJxKYI6sDXnBDIJbMvqwgWcyWmOvn2BypXNM4QnngC+OtoeqpNhslyQ\nHdm8mc+2+/blq86zZ3NLoKCgvKU2584Fhg419zDtVvyhC0jybVx8SrE5OTnxTKNPPil1V1dXYPBg\n4OefTTAue5aUxGcQWvp5qbO1+lz0e+IJTtL//rvGBk9PYPp0YMoUw4xXGFTD2CNIa9EBcHLSGthe\nuwa0b8/Z2e3bOQsaFAS8+SZw4AD/znV59VXgm290b8/J4ffY0qWc6X38cY3OCh4e2D5+Awac+ajI\nHB59Atv4eN7Pw4MD5zNngOTkku9jLBLYltXJk/grurXOsyZTCAwERr7ghhiXhsDp0+YbiDAZIuDT\nT/nr2jXOrqWm8hn9li3A4im3QcuW8VEwv4BKGN3Jk1zTCIDOnENuMzOVIZSkZ0/g3j29Mnjjx8vK\nrEa3cCGfgGqpq9SnDEFNUbimcs4cLVnbV1/l9K+6S4awGM3jDyGzPfcJ1QxsDx7kUsO33wZCQ/kk\n84svuLLosce4kUFJBg3iPrInT/Jnxpo1nAxRO3KEF1Dw9+f3jbd38b/3eI9AHGo9EZg6Nf82fQJb\ndbYW4JLJ9u05EDcHCWzLIj0ddPIklp3vhMceM+9QZs0CDmZ3xNWfpBzBHhw8yJehBj1GcHUF3nmH\ng9wOHYD+/YF3Mt/HmeCxQO/eFWrUL8pozhygTx/k3r4H9+hzcO1kho4IpXF0BJ5+Wq9UbEgIkJjI\ny+0KI7hyBVi+XGsNbGYmBwK9e+v/cAMHcsvkYpefK1Xi6fNvv20Za5yKfG1SDoI6c5/QwoFtQgJf\nMVm1Cnj55fI9tqMj8MorfO40eDBnfV97reA5tm5FfuyiKHwiq5nxz8oC9neaxm/GgwcB6BfYqutr\n1bp0MV+1pAS2ZXHkCO77t8ZDXarBw8O8Q6lWDWgyrhMurzoq1Qh2YPFi4MMhx+Hg48Vd2keM4BkD\n6elQoiIxKGUdnj07DRnd+8qqdKZCBBw/DvTqhfs9h6NzlZOo9ogFZmwB4JlnuM62lElkDg58iVKy\ntkby1lucCatVq9imgwe5ttbTU/+HU2dt587VEr8OGQJUry6/TEuSlYUWWSfh1LUjAC7/SUvj392O\nHVwer2/GXpcXXgD27QPatuUT1D59eN0ooGhgC/BzHT3KJ7OFhgilmivw8cccFWdl6dXuKyoKqF+/\n4Gd/f40aXhMqNbBVFKWOoih7FEU5ryjKWUVRJptiYBZp714ccOph1jKEwh7+X0e0zT6CFSvMPRJh\nTFevAtdCb2LImqEczG7YwMVRf/3FR5KnnoLTpIlo1csHn5zqA9q1q9wrUIkyiIoCHB2hWvojLsS4\no+2DvUBLCw1s27Th64N6tD0YN45jYDlhNiAi7s105QpfV9aiLGUIhfXtC9SsqSUhryh8WWfWLJ6K\nXwHmqpW0OSdP4joawdWPV99wcOAkVUoKNzMxRAt8b29uPThvHifuZ80CPv+cu7bcucNX+dSqVeMF\n67ZtK7gtf+WxESO47nHsWFRyUpWpFEE9DnNNRNUnY5sD4C0iagGgE4BXFUVpatxhWaacf/ZhVWQI\nhg8390iY0rQJvBzisekbM50WCaMjAuZNS8f2yk/C4dX/cZ+fVq2AZ5/lI2FoKH8avvMOFi0C/glv\ngBvx1bDvq7MS2xrbsWPAI4/g198dMC9oDejdqVzAZokUhbO2epQjBAZyfF6slZQon4MHOXr44APu\nGarZ/R5cZvTLL1wjWVaKwsm1t9/mYKbICUn79kCPHjyBsJyOH+e3dUJCuR9C5MkJPYRDSpcibwF3\ndw5Ed+yAwUocHQpFdk2bcnnLiBHcjVRz8tmTTxYtR4iNzZsYryh8vLh1C+PPvIGszJI/UDRLEXx8\nuLTfHEoNbInoDhGdyvs+BcBFAPqv/2Yr0tJAYWFw69cZNWuaezB5HBzg1PkRNLx9ECdPmnswopiX\nXuJPqwqYPRsYfmAyanVpxDOdNTVtyivGVK+OOnWAvXsB6t0Hh+f9XXy2tDCsY8dA7Ttg4ULgrXke\nUD76v6KfKJZm9GguxtSj0/orr3CyT06OKujaNW5fMGEC9+nr2LHYLkT8eg8axHFoeXTqxJed//2X\nL0FHRxfa+MEHPFW+nIs2bNvGq0h9/nn5xiYKqA4cxMnKXYp0vXB35xKBxo0BPz/jPO+sWcClS9oD\n58GDuXotMxM4dYq77zzzTN7GKlWAP/9Es3v78dAv00us146MLFqKYNGBbWGKotQH0AbAMWMMxqId\nPozzTm3w7Muu5h5JEcrYMZhZZTGW/iCfQBbl5k2eJLJ6dbkf4ocfgMgfd2NQpZ1wWv6DXj2AFAUI\neLEvxtTaVdGYWuiQnc3LWd7bcgxrrj4CRSlfps3kAgO50WVJ/YDyPPUUt+8xZy9Km7BlC19lGTuW\nW69p8dNP3BpJj45sJapThy/iDB3K59T5JyUBAXxS/OijKE8GZOdOnpn/zTeSta2Q3Fw4HtqPk+4h\nRW52d+fuBYYoQ9ClWTPg11+1P4ePD18E3L6dl8P95BN+L+Xz8MAXj/0Nr2tHOeWro77AkjK2ICK9\nvgBUA/AvgCd0bCdbdmfCDPrc/T3KyTH3SDTk5FBWo6Y0tNpOSkkx92BEvlmziJ57jsjNjejBgzLd\nrVMnooYNiQJ9UymzXkOiLVvK9twJCZTrWo283dIpNbWM4xal+vtvogZ1syjdyZXGPPmADh4094jK\n4No1Ii8vonPnSt31p5+IunYlUqlMMC5b1aMH0R9/6NwcHs6/jtOnDfeUWVlEbdoQrVqlsWHDBn6y\nlSv1fqz4eD6EpacTPf88H59EOf33H2U0aEpBQUVv7tuXCCA6c8Y8wyIi+uQToho1iJ54Qvvf+//+\nR/T159lE06cT1alDFBVVZHtiIlHVqkXvm5tL5ORElJlpnDHnxZxa41Xtp5AaFEVxArAJwE9E9Ieu\n/ebOnZv/fUhICEJCQsobb1uctK174fH4ghKbI5uFoyOc58/GwlfnYsP6Phj/vPGW8xV6yszkdOve\nvVw8tX07L+RdinXrgE0bVPjhB8DXzwH1v5kD57vty54OrF4dDsFtMC12NXbufEn70s+i3PbvB97q\ncxaVj9XHqt/czT2csmnYkHsAPfss1whrqfdUGzWKazZDQ7kNmCijxESuDSihf9fXX/Ms9tatDfe0\nzs7AsmXcCqx/f86cAQCGDwdatOCafB8fzr6VYvdu7qtauTIvaNahA/DGG2Xr3CDy7N6NB217oprG\n4pDu7nwJ35zzTocOBb7/nr+0XRh0dgayVE5c1qJS8b9LluRvV3dEKHxfBwdu1Rwbyx0SKmrfvn3Y\nt2+ffjvrinipaDZ2NYDFpexjnLDcAmTEJVMKXCnivIWmv3JyKKlOM3q96Q5zj0QQEf38M1GvXvz9\n998TjRpV6l2io4nqeaVSaqNWRI6OfPrs60t09275xnD+PKW6+dLiR38r3/2FTt26EZ2f9C2nsKyR\nSsWpmWnTSt11xQpOOopyWL+eaOBAnZtzcohq1ya6cME4Tz9lCtGTT1Lxq4yhoUS1ahHdvFnqY7zw\nAtHnnxf8PG4c0YcfGnacdqN/fzq3YDN17Vr05uefJ5o82TxDKqykKzNTphB99FHeD7Gx/PkUHZ2/\n/c8/iQYMKH6/1q2JTp407DjVUELGVp92X10APAOgp6IoYYqinFQUpX85g27Ll51drDXKodd+wdUa\nHVC/eVUzDaoUjo6o+n+zMS5iDv7ZJbW2Zvf117y4N8BFTTt2cBZXB5WKWyxtajQVVdu35JkaV6/y\nV366pYyaN0fqxq149sBLyNoqqw8ZSno6lykGxXNHBKukKAXpmTt3Stz12Wf5osPs2TKRrMz++qvE\nqy2hoYCvL9c/GsO8efxRNnKkxuHn0Ud5ttpzz5U4GYiI62v79i24bfz4Cs+HtU9ZWcChQ7gVFIJq\n1Ypuev11XnDH3EqawlFkgQYvL77M8NFH+ds1J46pmavOVp+uCIeIyJGI2hBRMBE9TEQ7TDE4s5gy\nBXj4YV6XDsCdPRfQ+pfpcFvxpZkHVjLHp0egQc1EbH5tn3wAmdPhwzxxTP2BVqsWd13fu1fr7rGx\n/MHT+u4utIv5nWdoODlxY0o3twoNxbtfW8xrtRk0ejQHyaLCjh/nq7nOJ48VbQhpbXx9OWr97LMS\nd3Ny4oXs/vgDePddCW71lpvLJUglBLZr13KjCmOpUoVjayI+v05NLbTxvfeAnBxeHuqJJzjI1ehx\nfOkS/9u0UHPPrl35mHXxovHGbZOOHQOCgpDo4AlXjfnnrVsDdeuaZ1j6cnbWaCP39tv8Br55E0Dx\niWNqPj7m6WVrwb1pzCAmhmexP/kk0KsXKDwCWU8Mx6HBH6HhExbaeF3NwQHV5k7Bc7f+D7/9Zu7B\n2CmVCpg8mdtvFZ4BrdkoEPxhs3EjH9Sa+ibg08TnoSxfDtSoYdAhBT3fDeubzuUmhhkZBn1sexQa\nCgxoH8etkyx1MQZ9vfMOF2OWMtXdx4fPy/bt47ZBQg9Hj3JhoY6IJTOTZ6mPGmXcYbi4AOvXA7Vr\nc+Y1/1ft6MgdG+bPB55/HujYETRiJE4EjcbAOmfw+az7WL82F/36Fa+bHDGCH1OUwZ49QM+eSElB\nscDWGhRbUtfXly8z5mVtr1wBGjQofj9vbwvN2NqVDz/kFPuiRcDIkVA1bY4wx7bov368uUemF4cx\nz+LhSuew5u0w5OSYezR2aMUKnoyT3wQwjzqwTUsDwJOPunThz5Rff8nCggvD4DByBK99aGAjRgBT\no/6HU6mNkP362wZ/fHtzYncSXv9nMC/mrqN9k9WoV4+bWOrR/svTk3ttLlnCrVlFKX79tcRs7bZt\n3HmtSFslI3FyAn78kS8whIQUWubU3Z2j3SeeQMTAV9HN+xKinBthszIMLywKwsyFlTEz4gXu+1bI\nqFEc2Er2vgx27wZ69UJqKoqVIliDYoEtwEtD//ILVP+F4dAhoHPn4vez2FIEuxEdzcVDU6YAAKLG\nzcHLrmvgu+lbuFS2kk4DLi5wmfomXk1dhOXLzT0YO5OYyJf3vvoqP8Vx7Rqvfrt0XxAu+ITg93YL\n0bQpn+j+73/AqTBCp2UTAA+PCq0MVBI/P+DsOQVfP7QMt5fvwNUPNxrleexBVlwSZh3sh6pdgive\ndNRSTJ3K79ki16m18/HhGfEzZ5pgXNZs40ZucTJhgs5d1q4tfv5rTA4OwOLF3J+4a1fgwoWCbQcP\n8gIPT411xbDz81HlxlVUy4xHRsx9BDStwrU3mzfn79+hA1/8OXPGdOO3aqmpXJjfrRtSU20kYwtw\n1nbRImQ++zy8q2dr7Xxgtl62umaVlfUL1t4V4aWX8mcJJyfzbL7Fi808pvJ48ICyq9ektjWu0/nz\n5h6MbdI6mfj113kKMfEs5I8/5paRw4YRTZhANH/iLUp386JLm89RdjbxFNSpU4k6diRTNZvd/dEJ\nilW86MDKayZ5PpuSnU2JwSG0oeYr3KDRlgwdqvfBLiWFZ/IfP27kMVmrv/4i8vEhOnWqxF18fblH\nrDmsXMnHpg0b+Mvbm2jnzhLucOQID3jfvvybpk7Vq6mG/UpNJerQgah+faJGjbiVChG99x7RvHlm\nHls5fPUV97ItRqWiqKZ9aWNb7a0y/viDaNAg44wJJXRFkMCWiGjJEsrx9qX7V+IoJ4doyBCi8eOt\nuDH5zJl0pfNYataMg3RhOBcucNPpNWsK3bh9O5G/P9G9exQZSdS9Ox/HwsM17vzNN9zxPjKSqHdv\nPvDdu2fC0RNde/1LOuX0MK1bmUH372tpBSS0e/dduh7Uj96YbGNBLREHYbVq6X2C9f333ALMao+P\nhrZnD9Hs2ZTYZxjdd/KmOf2P0mefaV+XJTSUA8mjR00/zML++49jLn9/orAwPe6wbRs35s87XoWF\nEQUG2t45nsHMn88njNev89/XrVtERPTmm7wYgrX5/nuiF1/Uvu3VxyIovVpNokuXim07fJg/5oxB\nAltdcnIo6YU36ZZbELV1v0I1anAL0W7diDIyzD24CkhKIvL3p4UDDtIzz8gHUIVlZBCNHk2q2XOo\nd2/+A/fy4viUoqOJfH1JtXdffibko490BIw5OfxXXq0a0QcfEKduTUylooReQ2mN52tUvTqRgwOv\nUvTbb/I+0en330lVrx71bB1Lv/9u7sEYydChRJ9+qteu2dlEzZpxts/u/fgjR4fvvUe/jVxHkwZH\n0tKlHPjPmFF017AwDmr/+cc8Q9WUmFjG8+p33+Vmpbm5pFIRPfII0bp1Rhue9bp5k8jTU0tmgy8M\nf/edGcZUQX/+yZ8TWVlFb1ep+Jw4bs6XRF26FDvTuXaNT6CMQQLbQqKjOXH2/ut36XTtfnTAKYQ+\nnHI/P1mRkWEjWax16yi31UPUpGE27d5t7sFYseRkoj59iB57jDKqeVKPJjcpO5uD15AuWaTq1Jki\nXv6ABg4katVKj6Uxo6OJLl40ydB1SkjgdMumTZSbW3DQCg7mg66Jk8iW7coVUnl70/yBR+jJJ204\nQ3X6NH9C6bku99GjfMX9xg0jj8uS/fgjZzEvXyYiXpPlzz9507lzRH5+Rc9dQ0KIli41wzgNJSuL\n1/t+9lmiu3dp1y6ixo3Nc35u0caO1VmnMXo0L1VtbVQqov79iebMKXr7lSv8J6DKyeXA9uuvCzb+\n+cETtHMAAB2MSURBVCel/biWqlYxTsZEAlsi2r2baPBgPpFa1HcXPXCrTWEDplPElazS72yNVCqi\nHj3oyOgvqXdvcw/GSp0/z2mJCRMo9UE2LXF/h2Ke4EKjnByizf6v0Z7KA6hRg1z68ksry/IfO8bp\no+vXiYgDti1beJE0d3f+/Bo+nEuHIyPNPFYTuX6ds5CHDvFS6FnXo4nq16dtw5ZRcLDeMZ/1euop\nLg7X0/z5HMzZbLCvi0rFy3EVCmrT0/lCTOHyg06duMaQiGuS69UrnvGyOg8e8PV0Ly9SffMthYRw\nfG/XYmOJvv2W6MsvOePh56e9DoV4wb9ffzXx+Azk5k0+mT12rOC2Zcs4WCciTth4efGB9O23ierX\nJ1XbtnRA6Uapx84afDx2EdjGx3Pd45Qp/Ob59FOiO3f4lzF8OFGDBvwHmLZlFxfCW8r1IGM6f55U\nXl401OeATPYozZ9/Ej30ENFLL1H2yjV0u+8YSq7qTV/UX0ytWqqoTh2iF4fE8plRRATRd99RdqOm\ntHN9gvVm+D/7jKh9e6LMzCI3pyTlUui2FFq3jmjiRP6AtvWszG+/8TH58cf5XKZ1rbt0CU1ortsn\n5O9PFBNj7hGawNmz/Mmlx1KrRPye6Ny5TLGw9UtP53VlW7cucql5926eB1rY8uWcTCHiz6DPPjPh\nOI3t7Fmixo3pynurKCDAyk7qDeXuXaJ33uHlZUePJnr1VaKXXyb6+2+dd+nalWjXLhOO0cDWrydq\n0oTLWIiIxozh+tt8H3xA5OrKVznj4ohycmhGjW8px9OLl5feuNFgbxabD2wTEjgmGTiQaOFCrvsZ\nN47Iw4Pfc++9R5SWRnxZuX59nuxjL/76i1LcfGlts3k2UmNRfrt2FT2RVqm47u232ScpuYoXfdhu\nE73v/Rn94fAEfe83hxa8k0i7d3Pt/+XLedmWWbM40vP1Jbp61Wz/F4NQqfgscOTIglmGqak8e7JK\nFaKFCyk3LYN69SJ6/33zDtWQsrN58sxffxFt2sTJhbp18yb0REXxWXFQEOXOnEU3bphv9rpZzJ9P\n9Oijep/JhIfzn4LmrHqbrNfev5+oXTuOUjXS9zNnFq+pTUnhz5/9+/mkyeYm8p4+TeTlRS88epkn\nRMXGEk2axMHNv/9yKj8722RdX0wmJ4fo669J5eVF1/q/Si/2jyZ3d6KgIKJ+/fiERpuICM6LWPuV\nn7feImrYkH/FAQEalXVZWRz9Foo12rYlOrEvhWjVKp5Z3aSJQcrxbDqwTUnhrMEbbxQ/mCYnayQf\nJk/mUww7k3r1Jh1w7kFJIYNsP/Wmw44dfKJTsyaXPy1dyn9wHercpLiqdWjz0xtp0yZORKSnl/BA\niYlcWLZ3r6mGblwpKdwCpHFjjk46dOAausuXOX3ZuDHd+eMoeXsTnTxp7sFWzIkTfOm8WjWi5s15\nHsyE/jF0OOg5ymzfmY/SNWsSPf88vxY2GZ2VIieHsy3Tp+t9l/37uarl7FlO/s+Zw8GuTWS5c3P5\n6l7v3lyXvny51vdFx46kdS7Dyy/zW0oz6LUZ33xD6c3a0NM1tlNaTX8ObCdP5uBFUXh2qrMz0ezZ\n5h6pYcTEELVrR7ldH6VRrc5Rt258Of7OHe6Y8/vvRE2bcmsszbKT6dM5TrEF69fzyZq3d+mHyQED\nuMwt37JlfEd1QXo52Wxge/EiH2/GjdNR55WVxTNh7t3jdJ2fH9H9+yYfpyV4f24WnfLpTarXbeQv\nqwzS0vgMc+tWLv/57Im99E+9cRTbpjepatWyrXRkea1dy5H/7NlFj1SbNxN5e9OBl1dTgwZ8yT7/\nb+3BAy5ItYIAcM13SfRplZl0r0EHSlqxicf8339cJ/neexydXb9uA0WQBnD3Lr8uZWh7sGYN15C2\nasV9KydOJHr6aSOOsYJSU/nq3jVdLZ0zMniHwED+Ty1dqvO98eABnyxpOyH+91+++HH7tuHGblFU\nKqIhQyirpi8N99hZNLhXZ+3u3i2oBbRmaWlE7dqRavYcGjdWRcOHaz/0JSZyMNezJzcoIuK3k4+P\n1o5YVuvaNf48KM3YsVqy2EeO8DEmMJBPpF9/nQvS1TUOerC5wHbrVr5aVqsWXxkukoRMSeHWNXXr\n8plizZp8auHrW1DJb4dSU4k6N4unBN/GGkUxtm/WLJ4XQxkZXBNVuzbP3tyxg8+OrCAwMwld2fxz\n50jVsCFF9hxPX/stoK985lNs1yeI3Nz4zPuHH0w7zpIcOcKBateuRA0bUsaAJ2ln67fpjqMfJQx+\njmu8HnqIa4u9vLgWQRT37798DJ06Ve+rPEuWEP38M/85paRwoBsaauRxlsPp09yurGdPDjZOfrCd\no3F1ijk9nevaBg7kNH8px4c//+TH0sXmS1kyMog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p4yuBAwdq6q0GWLvWj0XatoVDD4W8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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e998eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(m, _, s) = fit('en+Influenza', 104, 3,\n",
    "                sk.linear_model.ElasticNetCV(normalize=True, positive=True,\n",
    "                                             alphas=ALPHAS, l1_ratio=0.5,\n",
    "                                             max_iter=1e5, selection='random', n_jobs=-1))\n",
    "s.head(27)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the result tables next to one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">la_npa</th>\n",
       "      <th colspan=\"4\" halign=\"left\">en_npaa</th>\n",
       "      <th colspan=\"4\" halign=\"left\">en_npam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>input_ct</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rho</th>\n",
       "      <th>nonzero</th>\n",
       "      <th>input_ct</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rho</th>\n",
       "      <th>nonzero</th>\n",
       "      <th>input_ct</th>\n",
       "      <th>rmse</th>\n",
       "      <th>rho</th>\n",
       "      <th>nonzero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>0.85181</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>0.710605</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>0.738359</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>0.423396</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>162</td>\n",
       "      <td>0.389561</td>\n",
       "      <td>0.9</td>\n",
       "      <td>10</td>\n",
       "      <td>162</td>\n",
       "      <td>0.527247</td>\n",
       "      <td>0.5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>385</td>\n",
       "      <td>0.421616</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>385</td>\n",
       "      <td>0.390526</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "      <td>385</td>\n",
       "      <td>0.486709</td>\n",
       "      <td>0.5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>504</td>\n",
       "      <td>0.421472</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>504</td>\n",
       "      <td>0.390381</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "      <td>504</td>\n",
       "      <td>0.474364</td>\n",
       "      <td>0.5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>562</td>\n",
       "      <td>0.421593</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>562</td>\n",
       "      <td>0.390718</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "      <td>562</td>\n",
       "      <td>0.617935</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570</td>\n",
       "      <td>0.422126</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>570</td>\n",
       "      <td>0.390618</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "      <td>570</td>\n",
       "      <td>0.618032</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>571</td>\n",
       "      <td>0.422021</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>571</td>\n",
       "      <td>0.390572</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "      <td>571</td>\n",
       "      <td>0.617998</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>571</td>\n",
       "      <td>0.421677</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>571</td>\n",
       "      <td>0.390522</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13</td>\n",
       "      <td>571</td>\n",
       "      <td>0.61801</td>\n",
       "      <td>0.5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    la_npa                        en_npaa                         en_npam  \\\n",
       "  input_ct      rmse rho nonzero input_ct      rmse  rho nonzero input_ct   \n",
       "1       32   0.85181  -1       5       32  0.710605  0.9       5       32   \n",
       "2      162  0.423396  -1       6      162  0.389561  0.9      10      162   \n",
       "3      385  0.421616  -1       7      385  0.390526  0.9      13      385   \n",
       "4      504  0.421472  -1       7      504  0.390381  0.9      13      504   \n",
       "5      562  0.421593  -1       7      562  0.390718  0.9      13      562   \n",
       "6      570  0.422126  -1       7      570  0.390618  0.9      13      570   \n",
       "7      571  0.422021  -1       8      571  0.390572  0.9      13      571   \n",
       "8      571  0.421677  -1       7      571  0.390522  0.9      13      571   \n",
       "\n",
       "                          \n",
       "       rmse  rho nonzero  \n",
       "1  0.738359  0.5      11  \n",
       "2  0.527247  0.5      17  \n",
       "3  0.486709  0.5      23  \n",
       "4  0.474364  0.5      26  \n",
       "5  0.617935  0.5      18  \n",
       "6  0.618032  0.5      18  \n",
       "7  0.617998  0.5      18  \n",
       "8   0.61801  0.5      18  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([la_npa[0], en_npaa[0], en_npam[0]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions by distance filter next to one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(data, ds):\n",
    "   for d in ds:\n",
    "      D = collections.OrderedDict([('truth', TRUTH_FLU)])\n",
    "      D[d] = data[1][d]\n",
    "      pd.DataFrame(D).plot(figsize=(12,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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zMjEBPDyQbda2dZXWeBT9CNFJ0bneNjg2GHee3UGfun0AAO81ew+bbnI5gr7c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\n1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9cc2898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eEJMPTsYsn1lot7odEtMTVR4bU15gIFCviE6Q2bJCfsAArK2B0aOBZcvUEBjT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\no7g62BygpuVmmM745fx8VAw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BmTML7n+n3jtIzkjGySAl+ocZkNu3xQpj338v2utNmAAY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9a4ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n9m2gcS4T5tx9fRevY5XPrl+nDtC9u0xuWdGmjcQWAH74AZg/Xy7fW5yplNgK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\nIImtEEIIIYSoFv4FYMI/ZEyqDL0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9a4aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(la_npa, range(1, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BUaOAY8eADh3ED2w0T/thzKIkJgJOTqL9IKtnqfFwtq+ILVtERXfjLx4oXy0S69fLEyeT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\n7M2qsSXQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e9cca320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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48w9cfXoVq69xTyhDd/s2ULeQRSnvvLgDVxtXDGk0BAsvLiyw384O+Phj4K+/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\nBgrdc48pP/HyMg/dOXvWjJCvVMk8UCYgwCxXubJJev38TB2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e99adf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s2i7GZ/U+Q5mSZTR+/h9/FKN1iixzSiVJwuqeq7HZfzMuPr2o8esyzQoKAmrV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\nBUVq8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e98bbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(en_npaa, range(1, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KDdcdHYHbYXGwq2OXs40njzFW/hVXilBYYgvktvzK3wpM36RyKSzMlGtsDb0rAmOMsTJQ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\nDNH5yD0Y4j5S32HkePNNIOn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K7wu+vkQXLhCdPSvWs8tkRKP3ji4w0fSzo5+R3SI72ua7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\nQgghhCgU/h8T+8B5CRUgFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e99729e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tzpBTtWpAhw7q7UT25sIxoIRWbIUQlkKIq0KIm0KIu0KIxboIzBjdi7iHWqtq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\nE/W09ISC2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e98c56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gI4ASE1vGGGP5CrYiHAo/hIbYhNjY4s/nVgTGqqYnTwBPz6K362v3scKtCMZQsS1cLA0I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32e98c5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(en_npam, range(1, 4))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
